Airline Service Effectiveness: An Analysis Of Value Addition, Quality And Risk Perception.

Andotra, Neetu ; Pooja ; Gupta, Sanjana 等

The goal of airlines is to develop services which attract and keep customers satisfied, loyal and speak well of the airline which in turn would increase revenue, customer equity, market share and profitability. Comparative service analysis reveals that domestic airlines lag behind in terms of baggage facilities, on ground, in flight and other services with major international airlines. Logistic regression and ANOVA elicit frequency, income, nature of ticket bought are vital predictors of service quality satisfaction both in low and full cost airlines; risk perception significantly varies across airlines and physical risk supersede performance, psychological and social risks. Airline service effectiveness demands increase in infrastructure outlays, mergers to create service benchmarks and creating service orientation among internal suppliers across airlines.

Introduction

Economic growth world wide is associated with increasing share of services in GDP, investment and employment (Fisher 1935, Clark 1940, Kuznets 1957, Chenery 1960 and Fush 1968). In line with global trends, the service revolution (Gordon and Gupta 2004) in India, also connotes 'service--led' growth (Hansda 2002) witnessing 7.5 percent annual growth primarily by fast growth in communication, banking services, business services and community services (education and health) coupled with economic reforms and growth in foreign demand services exports. The share of services increased from thirty seven percent in 1980 to forty nine percent in 2002, while the share of manufacturing remained static at sixteen percent (Kochhar et al 2006).

Air travel in India which was perceived to be a "Maharaja" syndrome due to its prohibitive cost became open by the virtue of 'Air Corporation Act' 1953, when existed eight airlines were nationalised and merged into 'Indian Airlines' for domestic and 'Air India' for international operations. In 1986, the air taxi scheme was introduced, under which other airlines could run charter flights without fixed time schedule and issue of the tickets. In year 1994, eight new airlines namely, Jet Airways, Air Sahara, Indian International, Archana, East West, NEPC, Modiluft and Damania fling in the Indian skies. Today India's open sky has eleven major, thirteen low cost and near about fifty international airlines. The domestic air passenger traffic grew from 19.8 million from 2004-2005 to 27.5 million in 2005-2006.The number of people seeking pilot licenses multiplied three times from three hundred in April 2005 to one thousand forty five in April 2006. India has one hundred twenty five airports handling sixty million passengers and 1.3 million tones of cargo every year. The Indian civil aviation sector is witnessing double-digit growth from the existing twenty five to thirty percent in 2005-06 to expected twenty five percent annually growth for the next five years.

Service Quality in Airlines

In airline industry, service quality is being increasingly viewed as a competitive marketing strategy revolving around customer focus, innovation, creative service and striving towards service excellence. Airlines services despite being homogeneous, are generally characterized by customer segmentation, customised service, guarantees, continuous customer feedback and comprehensive measurement of company performance (Albrecht 1992) and its variants are being used by suppliers to gain competitive edge in the market place. Flight scheduling, ticket prices, in-flight services, employees attitudes, facilities and ticketing procedures are few key factors in determining the airline service quality and influence passengers' choice of airline.

Theoretically, Parasuaraman et al. (1985 and1990) have developed SERVQUAL scale and a conceptual framework called the 'GAPS' model which estimates the difference between expectations and perceptions of actual service quality performance on five parameters namely, tangibility, reliability, responsiveness, assurance and empathy. Fick and Ritchie (1991) and Kim (1997) improved QUALITOMETRO proposed by Franceschini and Rossetto (1987) and earlier scales and after applying to airline industry found that reliability, empathy and tangibles had the most significant impact on customer perception of service quality. Schvaneveldt (1991) evaluated service quality from two view points. First 'objective' include the presence or absence of a particular quality dimensions and the second 'subjective' include the users resulting sense of satisfaction or dissatisfaction. Cronin and Taylor (1992) designed SERVPREF based upon the revised version of SERVQUAL to measure customer evaluations of service quality including airline industry by including : baggage handling, bumping procedures, operations and safety, in flight comforts and connections. Since most of the travel experiences rely on intangible services, the travelers' perception is high and would influence their evaluation of airline selection. After 9/11 & SARS crisis, passengers' perceptions of selecting an airline has been changed. Cronin and Taylor (1994) added risk perception with SERVPREF to observe the behaviour of passengers in different environmental events. They categories risk perception as: financial risk, performance risk, physical risk, psychological risk, social risk and overall risk. During 1995-2003 various conceptual models of service quality (Parasuraman,A.2004) focused on multiple method listening : a SQ information, role of technology in service delivery, understanding and measuring e-service quality and network based customer system.

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Research Gap

Delivering high quality service to passengers is important for airlines to survive, gaining competitive advantages through repeated customer patronage, preferred transportation supplier status, market share gains and eventually increased profitability for the airlines. (Morash and Ozment 1994).The earlier researches related to service quality in airlines have applied service quality theories and methods in airline setting (Alotaibi 1992,Ostrowski et al 1993, Sultan and Simpson 2000, Chang and Yeh 2002,Tsaur et al 2002).The service quality gap model (Fick and Ritchie 1991, Gourdin and Kloppenborg 1991) used SERVQUAL scale to measure mean scores of consumer expectation and perception of service performance measures and SERVPERF (Cronin and Taylor 1992,1994) explained more of the variation in the global measures of service quality. In the present paper, several variables influencing flyers behaviour such as perceived price, perceived value, corporate image, risk, flyers satisfaction etc. ignored earlier are applied to public and private airlines operating from Jammu Aerodrome.

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Research Methodology

Hypotheses and Objectives

Value -added services are ways in which companies can gain competitive advantage in the airline industry (Dennett et al.2000). Proactive and adaptive service providers keep the customers satisfied through value added strategies (Jin et al. 2005) by the material, technology (Asian Business 1996), appearance of the personnel (Khan and Su 2003)and greater benefits to repeat passengers than to occasional passengers (Dube and Maute 1998). Future intentions and decisions to return to the service providers, customers are likely to consider whether or not they receive 'value for money' (Bolton and Drew 1991). Value has a direct impact on how satisfied customers are with a supplier offering which is a combination of fares and quality (Anderson et al.1994, Ravald and Gronross 1996, Limmink et al.1998 and Teboul 1991) The value of the airline service output increases when services are provided with added value to the tangible products. Thus, the paper hypothesises:

H1: Linear relationship exist between added value to tangible product and perceived value of service.

Obj1: To compare the service of Air India /Indian Airlines with major global airlines.

Obj2: To measure the level of satisfaction among the flyers regarding service quality rendered by domestic airlines.

Airline companies are putting emphasis on Customer Relationship Management (CRM) as a tool for managing customer relationships, customer satisfaction and loyalty (Khalifa and Liu 2003;Kotorov 2002; Park and Kim 2003; Ngai 2005) which consequently will increase steady stream of revenue, customer equity and market share (Wang et al 2004). Some studies have shown that key factors in determining airline service quality such as change planes, flight scheduling, ticket prices, in-flight service, employee attitudes, facilities, risk etc. are influenced by cultural background (Cunningham et al 2002) and nationality (Sultan and Simpson 2000 and Hoover, Green and Saegert 1978). Further to analyse the impact of demographic variables on service quality satisfaction among flyers, it is hypothesised:

H2: Demographic variables are vital predictors of service quality satisfaction in full and low cost airlines.

Ob2a: To assess the observed and predictive percentage of demographic variables in full and low cost carriers.

Ob2b: To ascertain the statistical significance of predictors of service quality in full cost and low cost carriers.

Safety culture is viewed an enduring characteristic of an organization that is reflected in its consistent way of dealing with critical safety issues (Zhang et al 2002) such as terrorism, industrial accidents and food quality. Safety especially within the aviation industry have remained 'unsystematic, fragmented, and in particular under specified in theoretical terms' (Pidgeon 1998). Safety components at global level are manifested in form of organizational commitment, management involvement, a fair evaluation and reward system, employee empowerment and an effective and systematic reporting system (Wiegmann et al 2002). Travel experiences rely on intangible services , it is expected that travelers' perceptions of risk are likely to be high, and such perceptions would influence their evaluations of the travel service (Moutinho 1987 and Sonmez and Graefe 1998).To assess risk perception among flyers as determinant of service quality, the paper hypothesises

H3: Risk perception behaviours significantly differ across airlines.

Ob3a: To assess difference in mean risk perception behaviour among flyers of airlines

Ob3b: To measure statistical significance of variance across airlines.

To test these hypotheses, comparative, logistic regression and ANOVA statistical methods were used.

Collection of Data

The survey was conducted through two self developed schedules each for air flyers and airline employees and contents were designed after reviewing the relevant literature. viz. Cuunigham et. al. 2004 (perceptions of airline service quality: pre and post 9/ 11), Bejou 1998 (service failure and service loyalty; an empirical study of airline customers), Peelen et. al. 2004 (differentiated approach to service recovery), Santos 2002 (from intangibility to tangibility on service quality perceptions) and Frost 2000 (service quality between internal customers and internal suppliers in an international airline). Pilot survey was conducted on 30 air waiting flyers and 10 air employees selected randomly from Jammu Aerodrome. After checking for the content validity and deleting erroneous statements, the finalised schedule for air flyers was divided into two parts: general information and information about service quality sub-divided into six dimensions namely, tangibility, reliability, responsiveness, assurance, empathy and perceived risk. Responses related to service quality variables were collected on five point Likert scale (5<--1>) where 5 denotes strongly agree (5) and 1 denotes strongly disagree. Similar scale was used in eliciting information about employee satisfaction among twenty eight employees of various airlines. On the basis of air traffic of approximately 1, 35,000, the optimum sample size was arrived at two hundred thirty seven (Mukhopadya 1998) for air flyers and twenty eight air employees. Secondary information was collected through books, journals and web search engines.

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Reliability and Validity

The reliability of scale items was tested using Cronbach's alpha method, the value of six factor ranged from 0.996 to 0.897 except for one factor (F7), indicating satisfactory internal consistency and also being above 0.77 obtained by Gordon and Naryanan (1984). The low reliability coefficient of factor 7 signifying extremely low variability due to vague, biased and similar responses of air flyers which arrived because of lack of interest and time constraint necessitating further improvements in scale items (Lyon et al. 2000). The value of Kaiser-Mayer-Olkin measure of sampling adequacy was 0.846 indicating that sample size is large enough to yield suitable and reliable factors and Bartlett's test of sphercity 18067.02 also authenticated that there is sufficient common variance in the factors (Table I). Predictive validity criteria is satisfied from the predictive ability of demographics towards service quality scale using Logistic regression.

Interpretation of Results

Comparative Value Added Services

A comparative value added service analysis (Table II) reveals that domestic airlines lag behind in terms of bagging handling, on ground services, in flight services, special service and TPT in comparison with international airlines. Early recovery of lost baggage and its compensation is needed in domestic airlines. Addition in on ground services such as time performance, limousine service, collaboration with railways and provision of an airport guide are also desired. Provision of inflight mobile technologies and laptops, wide variety of wines and food services, on board shopping and provision of oxygen and medical equipment for meeting emergencies needs addition to compete with international airlines.

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Demographic Variables as Predictors of Service Quality Satisfaction

In the Logistic regression (Table III), impact of demographic variables such as age, caste, gender, annual income, frequency of travel, purpose of travel, occupation etc. on satisfaction of five dimensions of service quality has been examined. In the equation, instead of predicting the value of a variable Y from a predictor variable X1 or several predictor variables (Xs), we predict the probability of Y occurring given known values of X1 (or Xs). In its simplest form, when there is only one predictor variable X1, the logistic regression equation from which the probability of Y is predicted is given by equation:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]

in which P(Y) is the probability of Y occurring, e is the base of natural logarithms, and the other coefficients form a linear combination. It is possible to extend this equation so as to include several predictors. When there are several predictors the equation becomes:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]

To investigate socio-demographic factors associated in achieving optimum service quality in airlines, step-wise logistic regression models were estimated. A comparison of the overall classification success with an arbitrary benchmark of fifty percent correct has been taken. In order to carry out a logistical regression , these dependent variables were split into "full cost" (code 1) and "low cost, stagnation or regression" (code 0). The classification table explains that 1st (use travel) and 3rd (annual income, use travel and nature) model are correctly classifying 75.6 percent of full cost and low cost cases and 2nd model (annual income & use travel) 71.4 percent. In the 1st and 3rd model, majority (114) of low cost airlines had maintained service quality (i.e. correctly classifies 82.6 percent of cases) and only thirty four full cost were interested in providing quality services to airlines customers and in the 2nd model 106 of low cost airlines (i.e. correctly classifies 76.8 percent of cases) were maintaining optimum service quality levels. Model chi-square statistic further assesses that how better a model predicts the outcome. The value of model chi-square statistic works on the principle that -2log likelihood in three models has reduced from 219.425 to 197.642. This reduction explains that model is better at predicting service quality of airlines using socio-demographics than it was before income and nature of airlines were added. All values were significant at 0.05 level. Effects of removal is explained by significance value of Log-likelihood ratio that is highly significant (p<0.05) for 1st and 2nd model which elucidates that the removing use travel and income from the model would have significant effect on the predictive ability of the model. In other words, these variables should not be removed. The other two measures of R2 that is Cox and Snell R Square (0.412) and Nagelkerke adjusted value (0.554) have shown fairly substantial difference. The former never exceeds 1 and higher values indicate greater model fit while later falls in the range of 0 to 1. When the regression coefficient (b) is large, the standard error (S.E) tends to become inflated, resulting in the Wald statistic being underestimated. Table explains that income, use travel and nature are significant predictors of service quality in full and low cost airlines as significance level of the Wald statistic is less than 0.05. With exp (B) one can be fairly confident that the population value of exp b lies between 1.409 and 6.285 in the third model. However, there is a one percent chance that a sample could give a confidence interval that 'misses' the true value. Finally chi value of Hosmer and Lameshow test explain how well the chosen model fits the data thereby indicating statistical difference in the distribution of the actual and predicted dependent value.

Risk Perception Behaviour Across Airlines

One-way ANOVA and Levene's test has been used to measure the significant mean differences across seven groups of airlines with respect to risk-taking ability of respondents. The values of one-way ANOVA and Levene's test ranged from highest of 7.440 and 19.109 to the lowest of 4.691 and 4.902 respectively. The results from Table IV clearly reveal significance value of Levene's statistic of homogeneity of variances and F (Robust tests of equality of means) statistically at 0.05 percent significance level, indicating significant variances across airline groups. The probability of F-ratio was also found to be less than 0.05 which indicates significant effect of groups on risk taking ability of airlines. Further the values of standard error of mean (SEm) also indicate that mean value are able to explain the results clearly.

Managerial Implication

The result of the study has methodological and managerial implications. It becomes imperative for airlines to understand the drivers of passenger's future behavioural intentions broadly on baggage facilities, on ground services, in flight services and other services to achieve high service quality, profitability and conteracting the threats of privatisation and globalisation. Specifically, the gray areas pointed by respondents of Indian Airlines are prior intimation regarding cancellation of flights (32 percent), time lag in connecting flights (23 percent), costly tickets (15 percent), need for in flights cleanliness (15 percent) and provision for expecting mothers (15 percent). The responses of Jet Airways flyers are time lag of more than one hour in connecting flights (34 percent), more in flight entertainment (33 percent) and provision for expecting mothers (33 percent). Passengers of Spicejet favoured lower price vis a vis other LCC (50 percent) and lowering in flight service (50 percent). 100 percent flyers of Air Deccan experienced high in flight food service rates. The response rate for lower ticket price compared to other FCC (86 percent) and need for customer orientation among employees (14 percent) was found among the flyers of King Fisher airlines. For Go Air, the responses are lower the price compared to the services they provide (79 percent and lower hidden charges including in flight and airport charges (21 percent). 63 percent and 21percent flyers for Air Sahara demanded lower in flight services especially food and entertainment (63 percent) and need for change in the behaviour of in flight cabin crew respectively. Lower in flight food and entertainment services (63 percent) and more customer orientation among in flight cabin crew (21 percent) are recorded from the flyers of Air Sahara. Flyers of all the airlines unilaterally agreed for more investment in infrastructure (more waiting lounges at airport), upgradation of existing waiting halls, establishment of tourist guiding centre, more luggage trolleys, valet parking facility, systematised security to reduce chaos and construction of more terminals. Strategically, formations of region-wise joint ventures and mergers can prove to be useful for domestic airlines. Long range service planning, periodic capacity building programmes, upgrading and reviewing service-mix strategy would help the airline in designing differentiated optimal service-mix. Incentives should be given to frequent air flyers falling in higher income brackets and flyers buying round trip tickets both in full and low cost carriers. Customer relationship management must be initiated and strategies should revolve around customers' perception about price, value, image, risk and repurchase intention. Advertising strategy focusing on images and experiences not met during the earlier travel can bridge the gap between passenger expectation and perceptions of service and building word of mouth communication. To tackle physical risks associated with air travel, airlines should invest in adhering safety norms, image building and instituting prompt grievance handling machinery. Passengers' compliments and complaints can be used as a source for reorienting service strategy. Further to survive and prosper in turbulent times, airlines should be adaptive to environmental changes and react to any contingency swiftly and sincerely.

Conclusion

The paper has focused on value addition to services, caring vital predictors of service quality satisfaction and minimizing physical risk associated with air travel. The survey was conducted when the tourist traffic was its peak (May-August, 2007), a longitudinal study can further be done by collecting samples in the remaining period to augment and generalize the findings. The results of the study are likely to be generalise to other service sectors which share similar characteristic, such as banking, health service, insurance etc. .

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Proof. Neetu Andotra Dept. of Commerce, University of Jammu.

Ms. Pooja Research Scholar, Dept. of Commerce, University of Jammu.

Ms. Sanjana Gupta Research Scholar, Dept. of Commerce, University of Jammu.

Table I: Service Quality Variables Purified Using Factor Analysis

Factor wise Mean St. Dew Factor Eigen
dimension Loading Value

Factor 1
* Airline 3.39 1.36 .879
 physical
 facilities are
 usually
 appealing
* Airline staff 3.60 1.48 .912
 is well
 dressed
* Waiting lounge 3.32 1.33 .929
 is comfortable
* Food & 3.31 1.31 .528
 beverage
 service in
 airline is
 good
* Baggage 3.48 1.22 .863
 handling
 is prompt
* Sufficient in 3.13 1.44 .868
 flight
 entertainment
 is provided
* Amenities 3.50 1.35 .946
 (like towel)
 provide in
 airline is
 clean
* You can trust 3.52 1.43 .891
 employees
* Employees are 3.25 1.28 .932
 polite and
 understanding
* Employees get 3.43 1.27 .597
 adequate
 support
* Proper 3.41 1.19 .836
 security
 arrangement
 exists for
 passengers
* Passengers are 3.17 1.44 .865
 compensated in
 case of delay,
 cancellation
 and changing
 routes
* Hidden costs 3.46 1.35 .941
 are made known
 to the
 customers
* Airline gives 3.63 0.93 .705
 you individual
 attention
* Airline serve 3.55 1.10 .747
 your interest
F 1 13.73
Factor 2

* During service 3.12 2.11 .862
 failure
 airline/
 operators
 communication
 is clear
* Questions were 2.93 0.78 .823
 asked to
 clarify the
 situation
* First contacted 2.86 0.68 .899
 employees solve
 your problem
 without
 further help
* Employees make 2.88 0.57 .943
 appropriate
 use of
 communication
 medium
* Communication 2.88 0.64 .925
 medium is
 professional
* Problems were 2.92 0.82 .925
 solved within
 reasonable
 time
* Employees gave 2.89 0.68 .868
 you feedback
* Employees 2.88 0.66 .925
 explained the
 situation
* Employees are 2.91 0.73 .839
 always willing
 to help
* Employees are 2.94 0.76 .848
 compensated
 for financial
 loss
F2 10.98
Factor 3
* The airline 3.64 0.94 .880
 keep promises
* The airline is 3.69 0.93 .891
 systematic and
 assuring
* Your choice to 3.88 1.03 .930
 use this
 airline is
 wise one
* You were right 3.78 1.00 .778
 in selecting
 this airline
* You always had 3.78 1.19 .864
 a good
 impression of
 this airline
* This airline 3.81 1.19 .889
 has a better
 image than its
 competitors
* You are 4.02 1.00 .657
 satisfied with
 your decision
 of
 repurchasing
 the same
 airline ticket
* You will 3.54 1.09 .781
 recommend this
 airline
 to others
F3 5.56
Factor 4
* Airline 3.24 1.26 .902
 destinations
 are sufficient
* Facilities 3.21 1.36 .821
 for
 connecting
 flights are
 readily
 available
* Customer 3.43 1.27 .516
 grievances
 are promptly
 handled
* Airline 3.21 1.26 .902
 serves your
 interest
* First aid kit 3.13 1.16 .890
 is carried on
 all flights
 of airline
F4 3.39
Factor 5
* Airline 3.52 1.04 .817
 tickets
 are readily
 available
* Employees 3.57 1.03 .787
 know what
 you need
F5 1.66
Factor 6
* Airline seats 3.59 3.03 .926
 have good
 pitch
* Employees get 3.52 3.02 .927
 adequate
 support
F6 1.44
Factor 7
* Tangibles 3.96 2.90 .730
 provided by
 airline is up
 to your
 expectations
* Airline has 3.75 1.27 .787
 special
 facilities
 handicapped
 passengers
F7 1.33
Grand Total 3.38 1.23

Factor wise Variance Cumulative Community Alpha
dimension explained % variance % coefficient

Factor 1 .797
* Airline
 physical
 facilities are
 usually
 appealing
* Airline staff .858
 is well
 dressed
* Waiting lounge .874
 is comfortable
* Food & .696
 beverage
 service in
 airline is
 good
* Baggage .835
 handling
 is prompt
* Sufficient in .820
 flight
 entertainment
 is provided
* Amenities .901
 (like towel)
 provide in
 airline is
 clean
* You can trust .830
 employees
* Employees are .878
 polite and
 understanding
* Employees get .777
 adequate
 support
* Proper .786
 security
 arrangement
 exists for
 passengers
* Passengers are .816
 compensated in
 case of delay,
 cancellation
 and changing
 routes
* Hidden costs .892
 are made known
 to the
 customers
* Airline gives .724
 you individual
 attention
* Airline serve .782
 your interest
F 1 24.29 24.29 .972
Factor 2

* During service .154
 failure
 airline/
 operators
 communication
 is clear
* Questions were .771
 asked to
 clarify the
 situation
* First contacted .863
 employees solve
 your problem
 without
 further help
* Employees make .925
 appropriate
 use of
 communication
 medium
* Communication .992
 medium is
 professional
* Problems were .927
 solved within
 reasonable
 time
* Employees gave .859
 you feedback
* Employees .906
 explained the
 situation
* Employees are .750
 always willing
 to help
* Employees are .785
 compensated
 for financial
 loss
F2 19.80 44.09 .897
Factor 3
* The airline 1.84
 keep promises
* The airline is 0.87
 systematic and
 assuring
* Your choice to .902
 use this
 airline is
 wise one
* You were right .746
 in selecting
 this airline
* You always had .797
 a good
 impression of
 this airline
* This airline .878
 has a better
 image than its
 competitors
* You are .893
 satisfied with
 your decision
 of
 repurchasing
 the same
 airline ticket
* You will .608
 recommend this
 airline
 to others
F3 16.76 60.86 .964
Factor 4
* Airline .897
 destinations
 are sufficient
* Facilities .733
 for
 connecting
 flights are
 readily
 available
* Customer .777
 grievances
 are promptly
 handled
* Airline .903
 serves your
 interest
* First aid kit .869
 is carried on
 all flights
 of airline
F4 8.73 69.59 .908
Factor 5
* Airline .875
 tickets
 are readily
 available
* Employees .875
 know what
 you need
F5 4.21 73.80 .934
Factor 6
* Airline seats .939
 have good
 pitch
* Employees get .985
 adequate
 support
F6 4.03 77.80 .996
Factor 7
* Tangibles .552
 provided by
 airline is up
 to your
 expectations
* Airline has .676
 special
 facilities
 handicapped
 passengers
F7 3.29 81.09 .384
Grand Total

Footnotes: KMO Value = .846; Bartlett's test of sphercity = 18067.02
df = 1081, Sig. = .000.

Extraction Method: Principal Component Analysis Varimax with Kaiser
Normalization Rotation converged in 3 iterations.

Table II: Comparison of Domestic Airlines with Major
International Airlines

Variable Domestic Airlines (Indian, Jet
 Airways, Air Sahara, Spicejet, Air
 Deccan, Kin Fisher, GoAir)

Baggage handling In case of lost baggage, it is marked
 within 24 hrs and return to customers.

On ground Service * E-ticketing facilities.
 * Comfortable waiting lounges.
 * Special care regarding time
 performance.
 * Availability of both full and low
 fare.
 * Special discount to frequent flyers.

In-flight Services * In seat videogames, video and
 audio channels, magazines etc.
 * Low calorie, diet food and route
 dedicated meal is provided.

Special Services * Special assistance to handicapped,
 dependent, unaccompanying
 minors and expectant mothers.

Total passenger 29,865,000
traffic (2006)

Variable International Airlines (Japan Airways,
 American Air, Qantas, Mexican Airways,
 Lufthansa Airways and South African Airlines

Baggage handling * Lost baggage is traced within 12-18 firs
 and delivered to customers wherever they
 needed.
 * In case of damage compensation is paid to
 the customers

On ground Service * Comfortable waiting lounges.
 * Special facilities to frequent flyer in
 waiting.
 * Timely performance.
 * Facilities of E-checking in and
 E-ticketing
 * Provision of an airport guide.
 * Valet parking facilities to treatment
 flyer.
 * Availability of both full and low fare.
 * Limousine services, if needed.
 * Direct check in, in case of connecting
 flights.
 * On line and SMS check-in.
 * Seminars for relaxed flying.
 * Collaboration with railways led to direct
 check in flight.
 * Individual attention to frequent flyers
 at the airport.

In-flight Services * In seat videogames, audio, 65 videos, 25
 T.V. channels on demand in 9 languages.
 * On board duty free shopping.
 * In flight mobile technologies and lap
 tops.
 * Award winning food services with
 multi-cuisine.
 * Wide variety of wines.
 * Provision and latest block bluster of
 different languages
 * On board duty-free shopping
 * Doctors on board.

Special Services * Special assistance to handicapped
 passengers, infants, unaccompanied
 minors, expectant mothers, cardiac
 patents & pets. Facilities of oxygen and
 other medical equipment is also available.

Total passenger 65,00,000-9,80,38,000
traffic (2006)

Source:--Official websites of respective airlines

Table III : Determinants of Service Quality Based on Cost Using
Step-Wise Logistic Regression Model

Classification
[table.sup.(a)]

Dependent Predicted Percentage
variable Overall service correct
outcome quality (overall)

Observed Full Low
(Steps) Mgt Mgt

1. Full cost 66 34 66.0
Low cost 24 114 82.6
 75.6

2. Full cost 64 36 64.0
Low cost 32 106 76.8
 71.4

3. Full cost 66 34 66.0
Low cost 24 114 82.6
 75.6

Classification Omnibus tests of
[table.sup.a] model coefficients

Dependent Chi-square df Sig.
variable
outcome

Observed
(Steps)

1. Full cost 104.420 (step) 1 0.000
Low cost 104.420 (block) 1 0.000
 104.420 (model) 1 0.000

2. Full cost 13.202 (step) 1 0.000
Low cost 117.622 (block) 2 0.000
 117.622 (model) 2 0.000

3. Full cost 8.581 (step) 1 0.003
Low cost 126.203 (block) 3 0.000
 126.203 (model) 3 0.000

Classification Model Hosmer and
[table.sup.a] summary Lameshow
 test

Dependent -21og Coz & Nagelkerk Chi df Sig.
variable likelihood Snell e R Square square
outcome R
 Square

Observed
(Steps)

1. Full cost 219.425 0.355 0.478 193.968 5 0.00
Low cost 0

2. Full cost 206.223 0.390 0.524 104.153 7 0.00
Low cost 0

3. Full cost 197.642 0.412 0.554 40.426 7 0.00
Low cost 0

 Variables in the equation

Dependent B S.E Wald df Sig. Ezpon
variables ential
 (B)

1. Use travel 0.831 0.102 66.372 1 0.000 2.295
 Constant -2.981 0.429 48.385 1 0.000 0.051

2. An. INC -0.974 0.284 11.742 1 0.001 0.378
 Use travel 0.880 0.107 67.332 1 0.000 2.412
 Constant -0.761 0.738 1.063 1 0.302 0.467

3. An, INC -0.904 0.292 9.585 1 0.002 0.405
 Use travel 0.874 0.109 64.789 1 0.000 2.397
 Nature 1.090 0.381 8.170 1 0.004 2.976
 Constant -2.397 0.962 6.204 1 0.013 0.091

Variables in the equation Model if term removed

Dependent Model log Change in df Sig. of
variables likelihood -21og the change
 likelihood

1. Use travel -161.992 104.420 1 0.000
Constant

2. An. INC -109.712 13.202 1 0.000
Use travel -158.238 110.253 1 0.000
Constant

3. An, INC -104.103 10.564 1 0.001
Use travel -151.500 105.357 1 0.000
Nature -103.111 8.581 1 0.003
Constant

Footnotes: [sup.(a)] the cut off value 0.50; * p<0.05

Table IV: Measuring the Airlines Group-Wise Impact on
Risk-Taking Ability Using ANOVA

 1. Financial risk is involved in choosing an airline

A. Descriptive statistics Airline groups

 Indian Jet Spicejet Air
 Airlines Airways Deccan

Mean 1.88 1.52 1.92 1.79
Standard deviation 1.08 0.91 1.35 1.34
Standard error 0.19 0.16 0.28 0.23
B. Test of homogeneity
 of variances
Levene statistic
C. ANOVA
F value

 2. Performance risk is involved in choosing an airline

Mean 2.12 1.85 2.67 2.29
Standard deviation 1.58 1.35 1.24 1.45
Standard error 0.27 0.23 0.25 0.25
B. Test of homogeneity
 of variances
Levene statistic
C. ANOVA
F value

 3. Physical risk is involved in choosing an airline

Mean 1.76 1.39 2.42 1.82
Standard deviation 1.15 0.93 1.38 1.19
Standard error 0.20 0.16 0.28 0.20
B. Test of homogeneity
 of variances
Levene statistic
C. ANOVA
 F value

 4. Psychological risk is involved in choosing an airline

Mean 1.85 1.76 2.54 1.56
Standard deviation 1.35 1.32 1.35 1.08
Standard error 0.23 0.23 0.28 0.19
B. Test of
 homogeneity of
 variances
Levene statistic
C. ANOVA
F value

 5. Social risk is involved in choosing an airline

Mean 1.70 1.64 2.25 1.56
Standard deviation 1.07 0.99 1.26 1.24
Standard error 0.19 0.17 0.26 0.21
B. Test of
 homogeneity of
 variances
Levene statistic
C. ANOVA
F value

 1. Financial risk is involved in choosing an airline

A. Descriptive statistics Airline groups

 Kingfisher Go air Air Total
 Sahara

Mean 1.09 1.85 2.88 1.84
Standard 0.29 1.55 1.79 1.36
Standard 4.94 0.23 0.31 8.85
B. Test of homogeneity
 of variances
Levene statistic 19.109 *

C. ANOVA

F value 6.06 *

 2. Performance risk is involved in choosing an airline

Mean 1.44 2.33 3.12 2.25
Standard 0.93 1.40 1.74 1.48
Standard 0.16 0.21 0.30 9.57
B. Test of homogeneity
 of variances
Levene statistic 4.902 *
C. ANOVA
F value 4.891 *

 3. Physical risk is involved in choosing an airline

Mean 1.26 2.33 2.85 1.98
Standard 0.51 1.35 1.71 1.32
Standard 8.76 0.20 0.30 8.57
B. Test of homogeneity
 of variances
Levene statistic 9.912 *
C. ANOVA
F value 7.440 *

 4. Psychological risk is involved in choosing an airline

Mean 1.38 1.78 2.79 1.92
Standard 0.92 1.47 1.75 1.41
Standard 0.16 0.22 0.30 9.16
B. Test of
 homogeneity of
 variances
Levene statistic 6.490 *
C. ANOVA
F value 4.691 *

 5. Social risk is involved in choosing an airline

Mean 1.29 1.80 2.94 1.87
Standard 0.68 1.56 1.74 1.36
Standard 0.12 0.23 0.30 8.83
B. Test of
 homogeneity of
 variances
Levene statistic 9.706 *
C. ANOVA
F value 6.087 *

Footnotes: * values are statistically significant
at 0.05 percent level.